c8b7517209
serviceCenter = DI-Container (Resolver, Registry, Context) fuer Service-Instanziierung serviceHub = Consumer-facing Aggregation (DB-Interfaces, Runtime-State, lazy Service-Resolution via serviceCenter) - modules/serviceHub/ erstellt: ServiceHub, PublicService, getInterface() - 22 Consumer-Dateien migriert (routes, features, tests): imports von modules.services auf serviceHub bzw. serviceCenter umgestellt - resolver.py: legacy fallback auf altes services/ entfernt - modules/services/ komplett geloescht (83 Dateien inkl. dead code mainAiChat.py) - pre-extraction: progress callback durch chunk-pipeline propagiert, operationType DATA_EXTRACT->DATA_ANALYSE fuer guenstigeres Modell
505 lines
20 KiB
Python
505 lines
20 KiB
Python
#!/usr/bin/env python3
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# Copyright (c) 2025 Patrick Motsch
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# All rights reserved.
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"""
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AI Model Selection Test - Prints prioritized fallback model lists for all interface calls
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Tests all main interface methods in interfaceAiObjects.py and shows which models
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are selected for each type of AI operation (text generation, image analysis,
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image generation, web research, etc.).
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"""
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import asyncio
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import os
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import sys
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import base64
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# Add the gateway to path (go up 2 levels from tests/functional/)
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_gateway_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", ".."))
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if _gateway_path not in sys.path:
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sys.path.insert(0, _gateway_path)
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from modules.serviceHub import getInterface as getServices
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from modules.datamodels.datamodelAi import (
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AiCallOptions,
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AiCallRequest,
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AiModelCall,
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OperationTypeEnum,
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PriorityEnum,
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ProcessingModeEnum,
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)
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from modules.datamodels.datamodelUam import User
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from modules.aicore.aicoreModelRegistry import modelRegistry
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from modules.aicore.aicoreModelSelector import modelSelector
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class ModelSelectionTester:
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def __init__(self) -> None:
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testUser = User(
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id="test_user_models",
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username="test_models",
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email="test@example.com",
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fullName="Test Models",
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language="en",
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mandateId="test_mandate",
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)
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self.services = getServices(testUser, None)
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async def initialize(self) -> None:
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from modules.serviceCenter.services.serviceAi.mainServiceAi import AiService
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from modules.interfaces.interfaceAiObjects import AiObjects
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self.services.ai = await AiService.create(self.services)
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self.aiObjects = await AiObjects.create()
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async def _printFallbackListWithContext(self, title: str, prompt: str, context: str, options: AiCallOptions) -> None:
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print(f"\n{'='*80}")
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print(f"{title}")
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print(f"{'='*80}")
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print(
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f"Operation={options.operationType.name}, Priority={options.priority.name}, ProcessingMode={options.processingMode.name}"
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)
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# Show context and prompt sizes
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promptSize = len(prompt.encode("utf-8"))
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contextSize = len(context.encode("utf-8"))
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totalSize = promptSize + contextSize
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print(f"Prompt size: {promptSize} bytes, Context size: {contextSize} bytes, Total: {totalSize} bytes")
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availableModels = modelRegistry.getAvailableModels()
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failoverModelList = modelSelector.getFailoverModelList(
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prompt=prompt,
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context=context,
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options=options,
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availableModels=availableModels,
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)
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if not failoverModelList:
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print("No suitable models found (capability filter returned empty list).")
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return
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print("Prioritized fallback model sequence (name | quality | speed | $/1k in | ctx | score):")
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for idx, m in enumerate(failoverModelList, 1):
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costIn = getattr(m, "costPer1kTokensInput", 0.0)
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# Calculate detailed score breakdown
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promptSize = len(prompt.encode("utf-8"))
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contextSize = len(context.encode("utf-8"))
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totalSize = promptSize + contextSize
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# Get detailed scoring
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sizeRating = modelSelector._getSizeRating(m, totalSize)
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processingModeRating = modelSelector._getProcessingModeRating(m.processingMode, options.processingMode)
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priorityRating = modelSelector._getPriorityRating(m, options.priority)
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totalScore = sizeRating + processingModeRating + priorityRating
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print(
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f" {idx:>2}. {m.displayName} | Q={getattr(m, 'qualityRating', 0)} | S={getattr(m, 'speedRating', 0)} | ${costIn:.4f} | ctx={getattr(m, 'contextLength', 0)} | score={totalScore:.3f}"
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)
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print(f" Size: {sizeRating:.3f}, ProcessingMode: {processingModeRating:.3f}, Priority: {priorityRating:.3f}")
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async def _printFallbackList(self, title: str, prompt: str, options: AiCallOptions) -> None:
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print(f"\n{'='*80}")
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print(f"{title}")
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print(f"{'='*80}")
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print(
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f"Operation={options.operationType.name}, Priority={options.priority.name}, ProcessingMode={options.processingMode.name}"
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)
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# Show context and prompt sizes
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context = "" # Currently using empty context
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promptSize = len(prompt.encode("utf-8"))
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contextSize = len(context.encode("utf-8"))
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totalSize = promptSize + contextSize
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print(f"Prompt size: {promptSize} bytes, Context size: {contextSize} bytes, Total: {totalSize} bytes")
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availableModels = modelRegistry.getAvailableModels()
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failoverModelList = modelSelector.getFailoverModelList(
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prompt=prompt,
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context=context,
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options=options,
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availableModels=availableModels,
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)
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if not failoverModelList:
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print("No suitable models found (capability filter returned empty list).")
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return
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print("Prioritized fallback model sequence (name | quality | speed | $/1k in | ctx | score):")
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for idx, m in enumerate(failoverModelList, 1):
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costIn = getattr(m, "costPer1kTokensInput", 0.0)
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# Calculate detailed score breakdown
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promptSize = len(prompt.encode("utf-8"))
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contextSize = len(context.encode("utf-8"))
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totalSize = promptSize + contextSize
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# Get detailed scoring
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sizeRating = modelSelector._getSizeRating(m, totalSize)
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processingModeRating = modelSelector._getProcessingModeRating(m.processingMode, options.processingMode)
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priorityRating = modelSelector._getPriorityRating(m, options.priority)
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totalScore = sizeRating + processingModeRating + priorityRating
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print(
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f" {idx:>2}. {m.displayName} | Q={getattr(m, 'qualityRating', 0)} | S={getattr(m, 'speedRating', 0)} | ${costIn:.4f} | ctx={getattr(m, 'contextLength', 0)} | score={totalScore:.3f}"
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)
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print(f" Size: {sizeRating:.3f}, ProcessingMode: {processingModeRating:.3f}, Priority: {priorityRating:.3f}")
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async def run(self) -> None:
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"""Test model selection for all interface methods."""
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print("=" * 100)
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print("AI INTERFACE MODEL SELECTION TEST")
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print("=" * 100)
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print("Testing model selection for all interface methods in interfaceAiObjects.py")
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print("=" * 100)
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# Test 1: Text Generation (call method)
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await self._testTextGeneration()
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# Test 2: Image Analysis (callImage method)
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await self._testImageAnalysis()
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# Test 3: Image Generation (generateImage method)
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await self._testImageGeneration()
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# Test 4: Web Search (searchWebsites method)
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await self._testWebSearch()
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# Test 5: Web Crawling (crawlWebsites method)
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await self._testWebCrawling()
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# Test 6: Web Research (webQuery method)
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await self._testWebResearch()
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# Test 7: Content Analysis with Chunking
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await self._testContentAnalysis()
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# Test 8: Website Selection
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await self._testWebsiteSelection()
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# Test 9: Actual Interface Calls
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await self._testActualInterfaceCalls()
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# Show model registry summary
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await self._showModelSummary()
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print("\n" + "=" * 100)
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print("ALL INTERFACE TESTS COMPLETED")
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print("=" * 100)
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async def _testTextGeneration(self) -> None:
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"""Test model selection for text generation calls."""
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print(f"\n{'='*80}")
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print("1. TEXT GENERATION (call method)")
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print(f"{'='*80}")
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# Test different text generation scenarios
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scenarios = [
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("Text Analysis", "Write a summary about artificial intelligence trends.", OperationTypeEnum.DATA_ANALYSE),
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("Planning Task", "Create a project plan for software development.", OperationTypeEnum.PLAN),
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("Analysis Task", "Analyze the pros and cons of cloud computing.", OperationTypeEnum.DATA_ANALYSE),
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]
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for title, prompt, operation_type in scenarios:
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options = AiCallOptions(
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operationType=operation_type,
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priority=PriorityEnum.BALANCED,
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processingMode=ProcessingModeEnum.ADVANCED,
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maxCost=0.05,
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maxProcessingTime=30,
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)
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await self._printFallbackList(f" {title}", prompt, options)
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async def _testImageAnalysis(self) -> None:
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"""Test model selection for image analysis calls."""
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print(f"\n{'='*80}")
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print("2. IMAGE ANALYSIS (callImage method)")
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print(f"{'='*80}")
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# Create a small test image (1x1 pixel PNG)
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test_image_data = base64.b64encode(b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00\x00\x01\x08\x02\x00\x00\x00\x90wS\xde\x00\x00\x00\tpHYs\x00\x00\x0b\x13\x00\x00\x0b\x13\x01\x00\x9a\x9c\x18\x00\x00\x00\nIDATx\x9cc```\x00\x00\x00\x04\x00\x01\xdd\x8d\xb4\x1c\x00\x00\x00\x00IEND\xaeB`\x82').decode('utf-8')
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options = AiCallOptions(
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operationType=OperationTypeEnum.IMAGE_ANALYSE,
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priority=PriorityEnum.BALANCED,
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processingMode=ProcessingModeEnum.ADVANCED,
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maxCost=0.02,
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maxProcessingTime=20,
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)
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prompt = "Describe what you see in this image."
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await self._printFallbackList(" Image Analysis", prompt, options)
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async def _testImageGeneration(self) -> None:
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"""Test model selection for image generation calls."""
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print(f"\n{'='*80}")
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print("3. IMAGE GENERATION (generateImage method)")
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print(f"{'='*80}")
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options = AiCallOptions(
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operationType=OperationTypeEnum.IMAGE_GENERATE,
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priority=PriorityEnum.QUALITY,
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processingMode=ProcessingModeEnum.DETAILED,
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maxCost=0.10,
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maxProcessingTime=60,
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)
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prompt = "A futuristic cityscape with flying cars and neon lights."
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await self._printFallbackList(" Image Generation", prompt, options)
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async def _testWebResearch(self) -> None:
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"""Test model selection for web research calls."""
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print(f"\n{'='*80}")
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print("6. WEB RESEARCH (webQuery method)")
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print(f"{'='*80}")
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options = AiCallOptions(
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operationType=OperationTypeEnum.WEB_SEARCH_DATA,
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priority=PriorityEnum.BALANCED,
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processingMode=ProcessingModeEnum.ADVANCED,
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maxCost=0.05,
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maxProcessingTime=30,
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)
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prompt = "What are the latest trends in artificial intelligence?"
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await self._printFallbackList(" Web Research", prompt, options)
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async def _testWebSearch(self) -> None:
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"""Test model selection for web search calls."""
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print(f"\n{'='*80}")
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print("4. WEB SEARCH (searchWebsites method)")
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print(f"{'='*80}")
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options = AiCallOptions(
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operationType=OperationTypeEnum.WEB_SEARCH_DATA,
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priority=PriorityEnum.BALANCED,
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processingMode=ProcessingModeEnum.BASIC,
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maxCost=0.01,
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maxProcessingTime=30,
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)
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prompt = "Search for artificial intelligence companies"
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await self._printFallbackList(" Web Search", prompt, options)
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async def _testWebCrawling(self) -> None:
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"""Test model selection for web crawling calls."""
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print(f"\n{'='*80}")
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print("5. WEB CRAWLING (crawlWebsites method)")
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print(f"{'='*80}")
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options = AiCallOptions(
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operationType=OperationTypeEnum.WEB_CRAWL,
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priority=PriorityEnum.BALANCED,
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processingMode=ProcessingModeEnum.BASIC,
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maxCost=0.02,
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maxProcessingTime=60,
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)
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prompt = "Crawl content from these URLs"
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await self._printFallbackList(" Web Crawling", prompt, options)
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async def _testContentAnalysis(self) -> None:
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"""Test model selection for content analysis with chunking."""
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print(f"\n{'='*80}")
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print("7. CONTENT ANALYSIS WITH CHUNKING")
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print(f"{'='*80}")
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# Test with large content to trigger chunking
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large_content = {
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"https://example.com/page1": "This is a large document about artificial intelligence. " * 1000,
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"https://example.com/page2": "This is another large document about machine learning. " * 1000,
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}
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options = AiCallOptions(
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operationType=OperationTypeEnum.DATA_ANALYSE,
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priority=PriorityEnum.BALANCED,
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processingMode=ProcessingModeEnum.ADVANCED,
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maxCost=0.10,
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maxProcessingTime=60,
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)
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prompt = "Analyze this content and provide key insights."
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await self._printFallbackList(" Content Analysis", prompt, options)
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async def _testWebsiteSelection(self) -> None:
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"""Test model selection for website selection."""
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print(f"\n{'='*80}")
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print("8. WEBSITE SELECTION (selectRelevantWebsites method)")
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print(f"{'='*80}")
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# This method uses webQuery internally, so it uses the same model selection as web research
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options = AiCallOptions(
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operationType=OperationTypeEnum.WEB_SEARCH_DATA,
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priority=PriorityEnum.BALANCED,
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processingMode=ProcessingModeEnum.ADVANCED,
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maxCost=0.03,
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maxProcessingTime=20,
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)
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prompt = "Select the most relevant websites from this list for AI research."
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await self._printFallbackList(" Website Selection", prompt, options)
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async def _testActualInterfaceCalls(self) -> None:
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"""Test actual interface calls to show real model selection."""
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print(f"\n{'='*80}")
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print("9. ACTUAL INTERFACE CALLS (Real Model Selection)")
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print(f"{'='*80}")
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# Test 1: Text generation call
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print("\n Testing: aiObjects.call() - Text Generation")
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try:
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request = AiCallRequest(
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prompt="Write a short summary about machine learning.",
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context="",
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options=AiCallOptions(
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operationType=OperationTypeEnum.DATA_ANALYSE,
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priority=PriorityEnum.BALANCED,
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processingMode=ProcessingModeEnum.ADVANCED,
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maxCost=0.05,
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maxProcessingTime=30,
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)
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)
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# Get the model selection that would be used
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availableModels = modelRegistry.getAvailableModels()
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failoverModelList = modelSelector.getFailoverModelList(
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prompt=request.prompt,
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context=request.context,
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options=request.options,
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availableModels=availableModels,
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)
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if failoverModelList:
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print(f" Selected model: {failoverModelList[0].displayName}")
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print(f" Fallback models: {[m.displayName for m in failoverModelList[1:3]]}")
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else:
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print(" No suitable models found")
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except Exception as e:
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print(f" Error: {e}")
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# Test 2: Image analysis call
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print("\n Testing: aiObjects.callImage() - Image Analysis")
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try:
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options = AiCallOptions(
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operationType=OperationTypeEnum.IMAGE_ANALYSE,
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priority=PriorityEnum.BALANCED,
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processingMode=ProcessingModeEnum.ADVANCED,
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maxCost=0.02,
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maxProcessingTime=20,
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)
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availableModels = modelRegistry.getAvailableModels()
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failoverModelList = modelSelector.getFailoverModelList(
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prompt="Describe this image",
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context="",
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options=options,
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availableModels=availableModels,
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)
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if failoverModelList:
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print(f" Selected model: {failoverModelList[0].displayName}")
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print(f" Fallback models: {[m.displayName for m in failoverModelList[1:3]]}")
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else:
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print(" No suitable models found")
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except Exception as e:
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print(f" Error: {e}")
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# Test 3: Image generation call
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print("\n Testing: aiObjects.generateImage() - Image Generation")
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try:
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options = AiCallOptions(
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operationType=OperationTypeEnum.IMAGE_GENERATE,
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priority=PriorityEnum.QUALITY,
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processingMode=ProcessingModeEnum.DETAILED,
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maxCost=0.10,
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maxProcessingTime=60,
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)
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availableModels = modelRegistry.getAvailableModels()
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failoverModelList = modelSelector.getFailoverModelList(
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prompt="A futuristic cityscape",
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context="",
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options=options,
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availableModels=availableModels,
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)
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if failoverModelList:
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print(f" Selected model: {failoverModelList[0].displayName}")
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print(f" Fallback models: {[m.displayName for m in failoverModelList[1:3]]}")
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else:
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print(" No suitable models found")
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except Exception as e:
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print(f" Error: {e}")
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# Test 4: Web research call
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print("\n Testing: aiObjects.webQuery() - Web Research")
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try:
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options = AiCallOptions(
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operationType=OperationTypeEnum.WEB_SEARCH_DATA,
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priority=PriorityEnum.BALANCED,
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processingMode=ProcessingModeEnum.ADVANCED,
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maxCost=0.05,
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maxProcessingTime=30,
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)
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availableModels = modelRegistry.getAvailableModels()
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failoverModelList = modelSelector.getFailoverModelList(
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prompt="What are AI trends?",
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context="",
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options=options,
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availableModels=availableModels,
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)
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if failoverModelList:
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print(f" Selected model: {failoverModelList[0].displayName}")
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print(f" Fallback models: {[m.displayName for m in failoverModelList[1:3]]}")
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else:
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print(" No suitable models found")
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except Exception as e:
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print(f" Error: {e}")
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async def _showModelSummary(self) -> None:
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"""Show summary of all available models and their capabilities."""
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print(f"\n{'='*80}")
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print("MODEL REGISTRY SUMMARY")
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print(f"{'='*80}")
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availableModels = modelRegistry.getAvailableModels()
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print(f"Total models available: {len(availableModels)}")
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# Group by connector type
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by_connector = {}
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for model in availableModels:
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connector_type = getattr(model, 'connectorType', 'unknown')
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if connector_type not in by_connector:
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by_connector[connector_type] = []
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by_connector[connector_type].append(model)
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print(f"\nModels by connector type:")
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for connector_type, models in by_connector.items():
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print(f" {connector_type}: {len(models)} models")
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for model in models:
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capabilities = getattr(model, 'capabilities', [])
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print(f" - {model.displayName}: {capabilities}")
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# Show operation type support
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print(f"\nOperation type support:")
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for op_type in OperationTypeEnum:
|
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supported_models = [m for m in availableModels if hasattr(m, 'operationTypes') and any(ot.operationType == op_type for ot in m.operationTypes)]
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print(f" {op_type.name}: {len(supported_models)} models")
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if supported_models:
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model_names = [m.displayName for m in supported_models[:3]] # Show first 3 models
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print(f" Models: {', '.join(model_names)}")
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async def main() -> None:
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tester = ModelSelectionTester()
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await tester.initialize()
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|
await tester.run()
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|
|
|
|
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if __name__ == "__main__":
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asyncio.run(main())
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